UK to modify laws for full testing, large grants for R&D

The UK announced that robocar testing will be legalized in January, similar to actions by many US states, but the first major country to do so. Of particular interest is the promise that fully autonomous vehicles, like Google’s no-steering-wheel vehicle, will have regulations governing their testing. Because the US states that wrote regulations did so before seeing Google’s vehicle, their laws still have open questions about how to test faster versions of it.

Combined with this are large research grant programs, on top of the £10M prize project to be awarded to a city for a testing project, and the planned project in Milton Keynes.

Jerusalem’s MobilEye going public in largest Israeli IPO

The leader in doing automated driver assist using cameras is Jerusalem’s MobilEye. This week they’re going public, to a valuation near $5B and raising over $600 million. MobilEye makes custom ASICs full of machine vision processing tools, and uses those to make camera systems to recognize things on the road. They have announced and demonstrated their own basic supervised self-driving car with this. Their camera, which is cheaper than the radar used in most fancy ADAS systems (but also works with radar for better results) is found in many high-end vehicles. They are a supplier to Tesla, and it is suggested that MobilEye will play a serious role in Tesla’s own self-driving plans.

As I have written, I don’t believe cameras are even close to sufficient for a fully autonomous vehicle which can run unmanned, though they can be a good complement to radar and especially LIDAR. LIDAR prices will soon drop to the low $thousands, and people taking the risk of deploying the first robocars would be unwise to not use LIDAR to improve their safety just to save a few thousand for early adopters.

Chinese search engine Baidu has robocar (and bicycle) project

Baidu is the big boy in Chinese search — sadly a big beneficiary of Google’s wise and moral decision not to be collaborators on massive internet censorship in China — and now it’s emulating Google in a big way by opening its own self-driving car project.

Various stories suggest a vehicle which involves regular handoff between a driver and the car’s systems, something Google decided was too risky. Not many other details are known.

Also rumoured is a project with bicycles. Unknown if that’s something like the “bikebot” concept I wrote about 6 years ago, where a small robot would clamp to a bike and use its wheels to deliver the bicycle on demand.

Why another search engine company? Well, one reason Google was able to work quickly is that it is the world’s #1 mapping company, and mapping plays a large role in the design of robocars. Baidu says it is their expertise in big data and AI that’s driving them to do this.

Velodyne has a new LIDAR

The Velodyne 64 plane LIDAR, which is seen spinning on top of Google’s cars and most of the other serious research cars, is made in small volumes and costs a great deal of money — $75,000. David Hall, who runs Velodyne, has regularly said that in volume it would cost well under $1,000, but we’re not there yet. He has released a new LIDAR with just 16 planes. The price, while not finalized, will be much higher than $1K but much lower than $75K (or even the $30K for the 32 plane version found on Ford’s test vehicle and some others.)

As a disclaimer, I should note I have joined the advisory board of Quanergy, which is making 8 plane LIDARs at a much lower price than these units.

Nissan goes back and forth on dates

Conflicting reports have come from Nissan on their dates for deployment. At first, it seemed they had predicted fairly autonomous cars by 2020. A later announcement by CEO Carlos Ghosn suggested it might be even earlier. But new reports suggest the product will be less far along, and need more human supervision to operate.

FBI gets all scaremongering

Many years ago, I wrote about the danger that autonomous robots could be loaded with explosives and sent to an address to wreak havoc. That is a concern, but what I wrote was that the greater danger could be the fear of that phenomenon. After all, car accidents kill more people every month in the USA than died at the World Trade Center 13 years ago, and far surpass war and terrorism as forms of violent death and injury in most nations for most of modern history. Nonetheless, an internal FBI document, released through a leak, has them pushing this idea along with the more bizarre idea that such cars would let criminals multitask more and not have to drive their own getaway cars. read more »

I have many more comments pending on my observations from the recent AUVSI/TRB Automated Vehicles Symposium, but for today I would like to put forward an observation I made about two broad schools of thought on the path of the technology and the timeline for adoption. I will call these the aggressive and conservative schools. The aggressive school is represented by Google, Induct (and its successors) and many academic teams, the conservative school involves car companies, most urban planners and various others.

The conservative (automotive) view sees this technology as a set of wheels that has a computer.

The aggressive (digital) school sees this as a computer that has a set of wheels.

The conservative view sees this as an automotive technology, and most of them are very used to thinking about automotive technology. For the aggressive school, where I belong, this is a computer technology, and will be developed — and change the world — at the much faster pace that computer technologies do.

Neither school is probably entirely right, of course. It won’t go as gung-ho as a smartphone, suddenly in every pocket within a few years of release, being discarded when just 2 years old even though it still performs exactly as designed. Nor will it advance at the speed of automotive technology, a world where electric cars are finally getting some traction a century after being introduced.

The conservative school embraces the 4 NHTSA Levels or 5 SAE levels of technology, and expects these levels to be a path of progress. Car companies are starting to sell “level 2” and working on “level 3” and declaring level 4 or 5 to be far in the future. Google is going directly to SAE level 4.

The two cultures do agree that the curve of deployment is not nearly-instant like a smartphone. It will take some time until robocars are a significant fraction of the cars on the road. What they disagree on is how quickly that has a big effect on society. In sessions I attended, the feeling that the early 2020s would see only a modest fraction of cars being self-driving meant to the conservatives that they would not have that much effect on the world.

In one session, it was asked how many people had cars with automatic cruise control (ACC.) Very few hands went up, and this is no surprise — the uptake of ACC is quite low, and almost all of it is part of a “technology package” on the cars that offer it. This led people to believe that if ACC, now over a decade old, could barely get deployed, we should not expect rapid deployment of more complete self-driving. And this may indeed be a warning for those selling super-cruise style products which combine ACC and lanekeeping under driver supervision, which is the level 2 most car companies are working on.

To counter this, I asked a room how many had ridden in Uber or its competitors. Almost every hand went up this time — again no surprise. In spite of the fact that Uber’s cars represent an insignificant fraction of the deployed car fleet. In the aggressive view, robocars are more a service than a product, and as we can see, a robocar-like service can start affecting everybody with very low deployment and only a limited service area.

This dichotomy is somewhat reflected in the difference between SAE’s Level 4 and NHTSA’s. SAE Level 4 means full driving (including unmanned) but in a limited service area or under other limited parameters. This is what Google has said they will make, this is what you see planned for services in campuses and retirement communities. This is where it begins, and grows one region at a time. NHTSA’s levels falsely convey the idea that you slowly move to fully automated mode and immediately do it over a wide service area. Real cars will vary as to what level of supervision they need (the levels) over different times, streets and speeds, existing at all the levels at different times.

Follow the conservative model and you can say that society will not see much change until 2030 — some even talk about 2040. I believe that is an error.

Another correlated difference of opinion lies around infrastructure. Those in the aggressive computer-based camp wish to avoid the need to change the physical infrastructure. Instead of making the roads smart, make the individual cars smart. The more automotive camp has also often spoken of physical changes as being more important, and also believes there is strong value in putting digital “vehicle to vehicle” radios in even non-robocars. The computer camp is much more fond of “virtual infrastructure” like the detailed ultra-maps used by Google and many other projects.

It would be unfair to claim that the two schools are fully stratified. There are researchers who bridge the camps. There are people who see both sides very well. There are “computer” folks working at car companies, and car industry folks on the aggressive teams.

The two approaches will also clash when it comes to deciding how to measure the safety of the products and how they should be regulated, which will be a much larger battle. More on that later.

In Berkeley, yesterday I attended and spoke at the “Robotics: Science and Systems” conference which had a workshop on autonomous vehicles. That runs to Wednesday, but overlapping and near SF Airport is the Automated Vehicles Symposium — a merger of the TRB (Transportation Research Board) and AUVSI conferences on the same topic. 500 are expected to attend.

Yesterday’s workshop was pretty good, with even a bit of controversy.

Yesterday saw:

Ed Olson on more of the lessons from aviation on handoff between automation and manual operation. This keeps coming up a a real barrier to some of the vehicle designs that have humans share the chores with the system.

Jesse Levinson of Stanford’s team showed some very impressive work in automatic calibration of sensors, and even fusion of LIDAR and camera data, aligning them in real time in spite of movement and latency. This work will make sensors faster, more reliable and make fusion accurate enough to improve perception.

David Hall, who runs Velodyne, spoke on the history of their sensors, and his plans for more. He repeated his prediction that in large quantities his sensor could cost only $300. (I’m a bit skeptical of that, but it could cost much, much less than it does today.) David made the surprising statement that he thinks we should make dedicated roads for the vehicles. (Surprising not just because I disagree, but because you could even get by without much LIDAR on such roads.)

Marco Panove of Stanford showed research they did on Taxi models from New York and Singapore. The economics look very good. Dan Fagnant also presented related research assuming an on-demand semi shared system with pickup stations in every TAZ. It showed minimal vacant miles but also minimal successful rideshare. The former makes sense when it’s TAZ to TAZ (TAZs are around a square mile) but I would have thought there would be more rideshare. The conclusion is that VMT go up due to empty miles, but that rideshare can partially compensate, though not as much as some might hope.

Ken Laberteaux of Toyota showed his research on the changing demographics of driving and suburbs. Conclusion: We are not moving back into the city, suburbanization is continuing. Finding good schools continues to drive people out unless they can afford private school are are childless.

The event had a 3-hour lunch break, where most went to watch some sporting event from Brazil. The Germans at the conference came back happier.

Some good technical talks presented worthwhile research

Sheng Zhao and a team from UC Riverside showed a method to get cm accuracy in position and even in pose (orientation) from cheap GPS receivers, by using improved math on phase-matching GPS. This could also be combined with cheap IMUs. Most projects today use very expensive IMUs and GPSs, not the cheap ones you find in your cell phone. This work may lead to being able to get reliable data from low cost parts.

Matthew Cornick and a team from Lincoln Lab at MIT showed very interesting work on using ground penetrating radar to localize. With GPR, you get a map of what’s below the road — you see rocks and material patterns down several feet. These vary enough, like the cracks and lines on a road, and so you can map them, and then find your position in that map — even if the road is covered in snow. While the radar units are today bulky, this offers the potential for operations in snow.

A team from Toyota showed new algorithms to speed up the creation of the super-detailed maps needed for robocars. Their algorithms are good at figuring out how many lanes there are and when they start and stop. This could make it much cheaper to build the ultramaps needed in an automatic way, with less human supervision.

The legal and policy sessions got more heated.

Bryant Walker Smith laid out some new proposals for how to regulate and govern torts about the vehicles.

Eric Feron of Georgia Tech made proposals for how to do full software verification. Today formally proving and analysing code for correctness takes 0.6 hours per line of code — it’s not practical for the 50 million line (or more) software systems in cars today. Jonathan argues it can be made cheaper, and should be done. Note that fully half the cost of developing the 787 aircraft was software verification!

The final session, on policy included:

Jane Lappin on how DoT is promoting research.

Steve Shladover on how we’re all way to optimistic on timelines, and that coming up with tests to demonstrate superior safety to humans is very far away, since humans run 65,000 hours between injury accidents.

Myself on why regulation should keep a light touch, and we should not worry too much about the Trolley Problem — which came up a couple of times.

Raj Rajkumar of CMU on the success they have had showing the CMU/GM car to members of congress.

In the last few months, I have found myself asked many times about a concept for solar roadways. Folks from Idaho proposing them have gotten a lot of attention with FHWA funding, a successful crowdfunding and even an appearance at Solve for X. Their plan is hexagonal modules with strong glass, with panels and electronics underneath, LED lights, heating elements for snow country and a buried conduit for power cables, data and water runoff. In addition, they hope for inductive charging plates for electric vehicles.

This idea has come up before, but since these folks built a small prototype, they generated tremendous attention. But they haven’t spoken at all about the cost, and that concerns me, because with all energy projects, the financial math is 99% of the issue. That’s true of infrastructure projects as well.

There are two concepts here. The first is, can you make a cost effective manufactured road panel? Roads are quite expensive today, but they are just asphalt gravel and other industrial materials whose cost is measured the range of $50 to $100 per ton. A chart from Florida suggests that basic rural asphalt roads cost about $9 per square foot, all-in, including labour and grading (it’s flat there) and about $4/square foot for milling and resurfacing. Roadway modules could be factory made (by robots) but still would require more labour to install, but I still think it is a very tall order for a manufactured surface to not cost a great deal more, even an order of magnitude more than plain road. Paved roads need maintenance, and that’s expensive. It is proposed that these panels would be cheaper to maintain as you just swap them out, but I am again skeptical of this math. Indeed, one of the major barriers to proposals for electric roads (which can charge cars) is that putting anything in the road makes it prohibitively more expensive to maintain.

I won’t say this is impossible — but it’s all about the math. We need to see math that would show that the modular manufactured pavement approach can compete. I’m happy for that math to include future technologies, like robot assembly and placement (though realize that we’ll probably see road construction with simpler materials also done by robots even sooner.) Let’s see the numbers, how cheap can it get?

All of this is without the solar panels inside (or the electronics.) Because the solar panels have their own math. The only synergy is this: If the modular roadway can be made so that it costs only a bit more than other approaches, it offers us “free land” to put the panels, and it’s connected land in long strips to run power wires.

How valuable is free land? Well, cropland in the USA costs an average of about 10 cents per square foot. 23 cents in California. 3 cents/square foot in the rural west. Much more, of course, in urban places. The land is not that important, so the other value comes from having a nice, manufactured place in which to put solar panels.

Today solar panels are still costly. They are just getting down (primarily thanks to cheap Chinese money) to our grid price. Trends suggest they will get lower and become cost effective as a variable source of power. But until they get really, really cheap, you want to use them most efficiently.

To use solar panels at their best, you don’t want to lie them flat (except in the tropics) but rather you want to tilt them just just a bit below the angle of your latitude. Conventional wisdom also points them south, though it’s actually better for the grid and most people’s power demands if you point them south-west, losing a few percent of their output but getting more of it to match peak demand. Putting them flat costs you 20 to 30% of their output. (You can also have them motorized and gain even more, but it’s usually not cost-effective, and will become less cost-effective as panels get cheaper and motors don’t.)

To use solar panels at their best, you also want to put them where it’s very sunny. Finally you want to first put them where the local power comes from coal. When you have gotten rid of most of the coal, you can start putting them elsewhere. You can put panels in less sunny places which have power from hydro, nuclear or natural gas, but you’re really wasting your money. The ideal places are Arizona and New Mexico, with tons of sun and lots of coal. And lots of cheap, fairly low-value land.

To be fair, the biggest cost of the panels will soon be the hardware they are mounted in, along with the wires and electronics to connect them, and so perhaps these road modules could compete by being cheap hardware for that. But it seems not too likely.

In cities, rooftops provide another source of free land, much of it slanted about right and pointed in roughly the right direction. With lower cost than tearing up roads. But to be fair, right now one of the bigger cost elements is getting permits to do the construction and electrical work. Roads are far from bureaucracy-free, but at least it scales — you get permits for a big project all at once, not one house at a time. But we can solve that problem for houses if we really want to as well.

So my challenge to the solar roadway team is to show us the math. No, we don’t need to see what it cost to make your prototypes. I am sure they are very expensive, but that’s beside the point. I want to see a plan for how low the cost can go in theory, even assuming future technologies. And compare that to how low the cost for the alternatives can go in theory. And then factor in how things don’t get to that theoretical point due to bureaucracy, unions and other practicalities. Compare panels in the road to panels by the side of the road, tilted and not being driven over. Look at what paved roads cost in practice to what they could cost in theory to get an idea of how close you can actually get, or come up with a really convincing reason why one approach is immune from the problems of another.

And if that math says yes, go at it. But if it doesn’t, focus on where the math tells you to go.